Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Motif yggdrasil: sampling from a tree mixture model
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
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We present a new Gibbs sampler algorithm with the motivation of finding motifs, representing candidate binding sites for transcription factors, in closely related species. Since much conservation here arises not from the existence of functional sites but simply from the lack of sufficient evolutionary divergence between the species, a conventional Gibbs sampler will fail. We compare the effectiveness against conventional methods on closely-related yeast sequences. Our algorithm is also applicable to single-species or phylogenetically-unrelated sequences, and has further improvements over previous Gibbs samplers, including accounting for correlations in the “background” model, an option to search for “dimers” (pairs of motifs with variable spacing), and a “tracking” strategy that allows us to assess the significance of candidate motifs.